"""simple docstring"""







import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__SCREAMING_SNAKE_CASE             =logging.get_logger(__name__)  # pylint: disable=invalid-name



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Dict ):
     warnings.warn(
         'The preprocess method is deprecated and will be removed in a future version. Please'
         ' use VaeImageProcessor.preprocess instead'	,     a__	,     )
     if isinstance(a__	,     torch.Tensor ):
          return image
     elif isinstance(a__	,     PIL.Image.Image ):
          lowercase_      :							int						=      [image]

     if isinstance(image[0]	,     PIL.Image.Image ):
          lowercase_      :							Optional[int]						=      image[0].size
          lowercase_      :							Tuple						=      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

          lowercase_      :							int						=      [np.array(i.resize((w, h)	,     resample=PIL_INTERPOLATION['lanczos'] ) )[None, :] for i in image]
          lowercase_      :							int						=      np.concatenate(a__	,     axis=0 )
          lowercase_      :							Optional[Any]						=      np.array(a__ ).astype(np.floataa ) / 255.0
          lowercase_      :							str						=      image.transpose(0	,     3	,     1	,     2 )
          lowercase_      :							List[Any]						=      2.0 * image - 1.0
          lowercase_      :							Dict						=      torch.from_numpy(a__ )
     elif isinstance(image[0]	,     torch.Tensor ):
          lowercase_      :							Union[str, Any]						=      torch.cat(a__	,     dim=0 )
     return image



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Optional[Any] ):






     if isinstance(a__	,     torch.Tensor ):
          return mask
     elif isinstance(a__	,     PIL.Image.Image ):
          lowercase_      :							Union[str, Any]						=      [mask]

     if isinstance(mask[0]	,     PIL.Image.Image ):
          lowercase_      :							Optional[int]						=      mask[0].size
          lowercase_      :							Any						=      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
          lowercase_      :							List[str]						=      [np.array(m.convert('L' ).resize((w, h)	,     resample=PIL_INTERPOLATION['nearest'] ) )[None, :] for m in mask]
          lowercase_      :							Union[str, Any]						=      np.concatenate(a__	,     axis=0 )
          lowercase_      :							List[Any]						=      mask.astype(np.floataa ) / 255.0
          lowercase_      :							List[Any]						=      0
          lowercase_      :							Optional[int]						=      1
          lowercase_      :							str						=      torch.from_numpy(a__ )
     elif isinstance(mask[0]	,     torch.Tensor ):
          lowercase_      :							Optional[int]						=      torch.cat(a__	,     dim=0 )
     return mask







class 			UpperCamelCase						(     lowercase_    ):
   lowercase				=    42
   lowercase				=    42
   def __init__(     self	,__UpperCamelCase	,__UpperCamelCase     )      -> Tuple:


        '''simple docstring'''


        super().__init__()
        self.register_modules(unet=__UpperCamelCase	,scheduler=__UpperCamelCase     )







   @torch.no_grad()
   def __call__(     self	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase = 250	,__UpperCamelCase = 0.0	,__UpperCamelCase = 10	,__UpperCamelCase = 10	,__UpperCamelCase = None	,__UpperCamelCase = "pil"	,__UpperCamelCase = True	,)      -> Union[ImagePipelineOutput, Tuple]:


        '''simple docstring'''


        lowercase_      :							List[str]						=      image

        lowercase_      :							Optional[int]						=      _preprocess_image(__UpperCamelCase     )
        lowercase_      :							str						=      original_image.to(device=self.device	,dtype=self.unet.dtype     )
        lowercase_      :							Union[str, Any]						=      _preprocess_mask(__UpperCamelCase     )
        lowercase_      :							Union[str, Any]						=      mask_image.to(device=self.device	,dtype=self.unet.dtype     )

        lowercase_      :							Dict						=      original_image.shape[0]

        # sample gaussian noise to begin the loop
        if isinstance(__UpperCamelCase	,__UpperCamelCase     ) and len(__UpperCamelCase     ) != batch_size:
             raise ValueError(
                 f'''You have passed a list of generators of length {len(__UpperCamelCase     )}, but requested an effective batch'''
                 f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''     )

        lowercase_      :							str						=      original_image.shape
        lowercase_      :							List[Any]						=      randn_tensor(__UpperCamelCase	,generator=__UpperCamelCase	,device=self.device	,dtype=self.unet.dtype     )

        # set step values
        self.scheduler.set_timesteps(__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,self.device     )
        lowercase_      :							Union[str, Any]						=      eta

        lowercase_      :							Optional[Any]						=      self.scheduler.timesteps[0] + 1
        lowercase_      :							List[str]						=      generator[0] if isinstance(__UpperCamelCase	,__UpperCamelCase     ) else generator
        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps     )     ):
             if t < t_last:
                  # predict the noise residual
                  lowercase_      :							int						=      self.unet(__UpperCamelCase	,__UpperCamelCase     ).sample
                  # compute previous image: x_t -> x_t-1
                  lowercase_      :							List[Any]						=      self.scheduler.step(__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase     ).prev_sample

             else:
                  # compute the reverse: x_t-1 -> x_t
                  lowercase_      :							Optional[Any]						=      self.scheduler.undo_step(__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase     )
             lowercase_      :							Dict						=      t

        lowercase_      :							str						=      (image / 2 + 0.5).clamp(0	,1     )
        lowercase_      :							List[str]						=      image.cpu().permute(0	,2	,3	,1     ).numpy()
        if output_type == "pil":
             lowercase_      :							List[Any]						=      self.numpy_to_pil(__UpperCamelCase     )

        if not return_dict:
             return (image,)

        return ImagePipelineOutput(images=__UpperCamelCase     )









"""simple docstring"""


import itertools
import math


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5     ,      int(math.sqrt(a__ ) + 1 )     ,      6 ):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :str						=			2
						while True:
												if is_prime(a__ ):
																		yield num
												num += 1


def        UpperCAmelCase      (					a__ = 1_00_01 ):





						'''simple docstring'''






						return next(itertools.islice(prime_generator()     ,      nth - 1     ,      a__ ) )


if __name__ == "__main__":
		print(F"""{solution() = }""")